Abstract:
AIM:A new automatic method for detecting specific points and lines (straight and curves) in dental panoramic radiographies (orthopantomographies) is proposed, where the human knowledge is mapped to the automatic system. The goal is to compute relevant mandibular indices (Mandibular Cortical Width, Panoramic Mandibular Index, Mandibular Ratio, Mandibular Cortical Index) in order to detect the thinning and deterioration of the mandibular bone. Data can be stored for posterior massive analysis. METHODS:Panoramic radiographies are intrinsically complex, including: artificial structures, unclear limits in bony structures, jawbones with irregular curvatures and intensity levels, irregular shapes and borders of the mental foramen, irregular teeth alignments or missing dental pieces. An intelligent sequence of linked imaging segmentation processes is proposed to cope with the above situations towards the design of the automatic segmentation, making the following contributions: (i) Fuzzy K-means classification for identifying artificial structures; (ii) adjust a tangent line to the lower border of the lower jawbone (lower cortex), based on texture analysis, grey scale dilation, binarization and labelling; (iii) identification of the mental foramen region and its centre, based on multi-thresholding, binarization, morphological operations and labelling; (iv) tracing a perpendicular line to the tangent passing through the centre of the mental foramen region and two parallel lines to the tangent, passing through borders on the mental foramen intersected by the perpendicular; (v) following the perpendicular line, a sweep is made moving up the tangent for detecting accumulation of binary points after applying adaptive filtering; (vi) detection of the lower mandible alveolar crest line based on the identification of inter-teeth gaps by saliency and interest points feature description. RESULTS:The performance of the proposed approach was quantitatively compared against the criteria of expert dentists, verifying also its validity with statistical studies based on the analysis of deterioration of bone structures with different levels of osteoporosis. All indices are computed inside two regions of interest, which tolerate flexibility in sizes and locations, making this process robust enough. CONCLUSIONS:The proposed approach provides an automatic procedure able to process with efficiency and reliability panoramic X-Ray images for early osteoporosis detection.
journal_name
Artif Intell Medjournal_title
Artificial intelligence in medicineauthors
Aliaga I,Vera V,Vera M,García E,Pedrera M,Pajares Gdoi
10.1016/j.artmed.2020.101816subject
Has Abstractpub_date
2020-03-01 00:00:00pages
101816eissn
0933-3657issn
1873-2860pii
S0933-3657(19)30421-Xjournal_volume
103pub_type
杂志文章abstract:BACKGROUND:The current situation of critical progression in resistance to more effective antibiotics has forced the reuse of old highly toxic antibiotics and, for several reasons, the extension of the indications of combined antibiotic therapy as alternative options to broad spectrum empirical mono-therapy. A key aspec...
journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章,多中心研究
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journal_title:Artificial intelligence in medicine
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
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更新日期:2015-01-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2017.06.007
更新日期:2017-11-01 00:00:00
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journal_title:Artificial intelligence in medicine
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doi:10.1016/j.artmed.2015.11.003
更新日期:2018-11-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
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更新日期:2020-01-01 00:00:00
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
doi:10.1016/j.artmed.2016.04.004
更新日期:2016-05-01 00:00:00
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journal_title:Artificial intelligence in medicine
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